24 citations · 25 across the 5 of their papers we have counts for
8 papers
Model Predictive Control of Spreading Processes via Sparse Resource Allocation
Ruigang Wang, Armaghan Zafar, Ian R. Manchester
In this paper, we propose a model predictive control (MPC) method for real-time intervention of spreading processes, such as epidemics and wildfire, over large-scale networks. The…
Contraction-Based Methods for Stable Identification and Robust Machine Learning: a Tutorial
Ian R. Manchester, Max Revay, Ruigang Wang
This tutorial paper provides an introduction to recently developed tools for machine learning, especially learning dynamical systems (system identification), with stability and rob…
Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics
Ruigang Wang, Patrick J. W. Koelwijn, Ian R. Manchester +1
In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gy…
Reduced-Order Nonlinear Observers via Contraction Analysis and Convex Optimization
Bowen Yi, Ruigang Wang, Ian R. Manchester
In this paper, we propose a new approach to design globally convergent reduced-order observers for nonlinear control systems via contraction analysis and convex optimization. Despi…
Lipschitz Bounded Equilibrium Networks
Max Revay, Ruigang Wang, Ian R. Manchester
This paper introduces new parameterizations of equilibrium neural networks, i.e. networks defined by implicit equations. This model class includes standard multilayer and residual…
On necessary conditions of tracking control for nonlinear systems via contraction analysis
Bowen Yi, Ruigang Wang, Ian R. Manchester
In this paper we address the problem of tracking control of nonlinear systems via contraction analysis. The necessary conditions of the systems which can achieve universal asymptot…